Increasing demands on the world’s resources require the design of off-highway machines that provide greater functionality and productivity along with greater efficiency. Model-based or virtual design provides a means for achieving these design improvements with reduced time and costs. However, virtual design is often limited by the fidelity with which human operators are modeled. A greater understanding of how highly skilled operators obtain high machine performance and productivity can inform machine development and advance agricultural and construction machine automation technology. This research investigated how machine operator expertise, strategies, and decision-making can be integrated into operator models that simulate authentic human behavior in construction machine operations. The initial effort of this work was to develop a virtual operator model (VOM) through a combination of human factors and physical system modeling techniques. Operator interviews were conducted to build a framework of tasks, strategies, and cues commonly used while controlling an excavator through repeated work cycles. A closed loop simulation demonstrated that a VOM could simulate the trenching work cycle and enable closed-loop virtual equipment operation simulation. Advancing the state of the art in operator modeling requires models that can adapt. Approaches to enable a generic virtual operator model to adapt to changes in the environment based on the operator’s actions were investigated. The closed loop simulation performed successfully when using the VOM, the vehicle model, and an environment model which represented how the VOM adapted during a complete trenching operation.